598 lines
20 KiB
Python
598 lines
20 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import Callable
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from paddle import ParamAttr
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from paddle.nn.initializer import KaimingNormal
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import numpy as np
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import paddle
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import paddle.nn as nn
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from paddle.nn.initializer import TruncatedNormal, Constant, Normal
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trunc_normal_ = TruncatedNormal(std=.02)
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normal_ = Normal
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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def drop_path(x, drop_prob=0., training=False):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
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"""
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if drop_prob == 0. or not training:
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return x
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keep_prob = paddle.to_tensor(1 - drop_prob)
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shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
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random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
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random_tensor = paddle.floor(random_tensor) # binarize
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output = x.divide(keep_prob) * random_tensor
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return output
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=0,
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bias_attr=False,
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groups=1,
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act=nn.GELU):
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super().__init__()
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self.conv = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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weight_attr=paddle.ParamAttr(
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initializer=nn.initializer.KaimingUniform()),
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bias_attr=bias_attr)
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self.norm = nn.BatchNorm2D(out_channels)
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self.act = act()
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def forward(self, inputs):
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out = self.conv(inputs)
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out = self.norm(out)
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out = self.act(out)
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return out
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class DropPath(nn.Layer):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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class Identity(nn.Layer):
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def __init__(self):
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super(Identity, self).__init__()
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def forward(self, input):
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return input
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class Mlp(nn.Layer):
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def __init__(self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class ConvMixer(nn.Layer):
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def __init__(
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self,
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dim,
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num_heads=8,
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HW=[8, 25],
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local_k=[3, 3], ):
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super().__init__()
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self.HW = HW
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self.dim = dim
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self.local_mixer = nn.Conv2D(
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dim,
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dim,
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local_k,
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1, [local_k[0] // 2, local_k[1] // 2],
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groups=num_heads,
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weight_attr=ParamAttr(initializer=KaimingNormal()))
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def forward(self, x):
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h = self.HW[0]
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w = self.HW[1]
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x = x.transpose([0, 2, 1]).reshape([0, self.dim, h, w])
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x = self.local_mixer(x)
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x = x.flatten(2).transpose([0, 2, 1])
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return x
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class Attention(nn.Layer):
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def __init__(self,
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dim,
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num_heads=8,
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mixer='Global',
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HW=[8, 25],
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local_k=[7, 11],
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim**-0.5
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self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.HW = HW
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if HW is not None:
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H = HW[0]
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W = HW[1]
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self.N = H * W
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self.C = dim
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if mixer == 'Local' and HW is not None:
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hk = local_k[0]
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wk = local_k[1]
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mask = np.ones([H * W, H * W])
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for h in range(H):
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for w in range(W):
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for kh in range(-(hk // 2), (hk // 2) + 1):
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for kw in range(-(wk // 2), (wk // 2) + 1):
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if H > (h + kh) >= 0 and W > (w + kw) >= 0:
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mask[h * W + w][(h + kh) * W + (w + kw)] = 0
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mask_paddle = paddle.to_tensor(mask, dtype='float32')
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mask_inf = paddle.full([H * W, H * W], '-inf', dtype='float32')
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mask = paddle.where(mask_paddle < 1, mask_paddle, mask_inf)
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self.mask = mask.unsqueeze([0, 1])
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self.mixer = mixer
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def forward(self, x):
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if self.HW is not None:
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N = self.N
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C = self.C
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else:
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_, N, C = x.shape
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qkv = self.qkv(x).reshape((0, N, 3, self.num_heads, C //
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self.num_heads)).transpose((2, 0, 3, 1, 4))
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q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
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attn = (q.matmul(k.transpose((0, 1, 3, 2))))
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if self.mixer == 'Local':
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attn += self.mask
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attn = nn.functional.softmax(attn, axis=-1)
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attn = self.attn_drop(attn)
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x = (attn.matmul(v)).transpose((0, 2, 1, 3)).reshape((0, N, C))
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Layer):
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def __init__(self,
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dim,
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num_heads,
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mixer='Global',
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local_mixer=[7, 11],
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HW=[8, 25],
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mlp_ratio=4.,
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qkv_bias=False,
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qk_scale=None,
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drop=0.,
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attn_drop=0.,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer='nn.LayerNorm',
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epsilon=1e-6,
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prenorm=True):
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super().__init__()
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if isinstance(norm_layer, str):
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self.norm1 = eval(norm_layer)(dim, epsilon=epsilon)
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elif isinstance(norm_layer, Callable):
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self.norm1 = norm_layer(dim)
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else:
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raise TypeError(
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"The norm_layer must be str or paddle.nn.layer.Layer class")
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if mixer == 'Global' or mixer == 'Local':
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self.mixer = Attention(
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dim,
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num_heads=num_heads,
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mixer=mixer,
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HW=HW,
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local_k=local_mixer,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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attn_drop=attn_drop,
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proj_drop=drop)
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elif mixer == 'Conv':
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self.mixer = ConvMixer(
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dim, num_heads=num_heads, HW=HW, local_k=local_mixer)
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else:
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raise TypeError("The mixer must be one of [Global, Local, Conv]")
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
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if isinstance(norm_layer, str):
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self.norm2 = eval(norm_layer)(dim, epsilon=epsilon)
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elif isinstance(norm_layer, Callable):
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self.norm2 = norm_layer(dim)
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else:
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raise TypeError(
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"The norm_layer must be str or paddle.nn.layer.Layer class")
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp_ratio = mlp_ratio
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self.mlp = Mlp(in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_layer=act_layer,
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drop=drop)
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self.prenorm = prenorm
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def forward(self, x):
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if self.prenorm:
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x = self.norm1(x + self.drop_path(self.mixer(x)))
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x = self.norm2(x + self.drop_path(self.mlp(x)))
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else:
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x = x + self.drop_path(self.mixer(self.norm1(x)))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class PatchEmbed(nn.Layer):
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""" Image to Patch Embedding
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"""
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def __init__(self,
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img_size=[32, 100],
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in_channels=3,
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embed_dim=768,
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sub_num=2):
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super().__init__()
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num_patches = (img_size[1] // (2 ** sub_num)) * \
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(img_size[0] // (2 ** sub_num))
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self.img_size = img_size
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self.num_patches = num_patches
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self.embed_dim = embed_dim
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self.norm = None
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if sub_num == 2:
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self.proj = nn.Sequential(
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ConvBNLayer(
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in_channels=in_channels,
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out_channels=embed_dim // 2,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=None),
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ConvBNLayer(
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in_channels=embed_dim // 2,
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out_channels=embed_dim,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=None))
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if sub_num == 3:
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self.proj = nn.Sequential(
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ConvBNLayer(
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in_channels=in_channels,
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out_channels=embed_dim // 4,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=None),
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ConvBNLayer(
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in_channels=embed_dim // 4,
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out_channels=embed_dim // 2,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=None),
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ConvBNLayer(
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embed_dim // 2,
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embed_dim,
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in_channels=embed_dim // 2,
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out_channels=embed_dim,
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kernel_size=3,
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stride=2,
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padding=1,
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act=nn.GELU,
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bias_attr=None))
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def forward(self, x):
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B, C, H, W = x.shape
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x).flatten(2).transpose((0, 2, 1))
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return x
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class SubSample(nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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types='Pool',
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stride=[2, 1],
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sub_norm='nn.LayerNorm',
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act=None):
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super().__init__()
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self.types = types
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if types == 'Pool':
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self.avgpool = nn.AvgPool2D(
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kernel_size=[3, 5], stride=stride, padding=[1, 2])
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self.maxpool = nn.MaxPool2D(
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kernel_size=[3, 5], stride=stride, padding=[1, 2])
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self.proj = nn.Linear(in_channels, out_channels)
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else:
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self.conv = nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size=3,
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stride=stride,
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padding=1,
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weight_attr=ParamAttr(initializer=KaimingNormal()))
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self.norm = eval(sub_norm)(out_channels)
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if act is not None:
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self.act = act()
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else:
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self.act = None
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def forward(self, x):
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if self.types == 'Pool':
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x1 = self.avgpool(x)
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x2 = self.maxpool(x)
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x = (x1 + x2) * 0.5
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out = self.proj(x.flatten(2).transpose((0, 2, 1)))
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else:
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x = self.conv(x)
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out = x.flatten(2).transpose((0, 2, 1))
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out = self.norm(out)
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if self.act is not None:
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out = self.act(out)
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return out
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class SVTRNet(nn.Layer):
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def __init__(
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self,
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img_size=[32, 100],
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in_channels=3,
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embed_dim=[64, 128, 256],
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depth=[3, 6, 3],
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num_heads=[2, 4, 8],
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mixer=['Local'] * 6 + ['Global'] *
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6, # Local atten, Global atten, Conv
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local_mixer=[[7, 11], [7, 11], [7, 11]],
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patch_merging='Conv', # Conv, Pool, None
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mlp_ratio=4,
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qkv_bias=True,
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qk_scale=None,
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drop_rate=0.,
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last_drop=0.1,
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attn_drop_rate=0.,
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drop_path_rate=0.1,
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norm_layer='nn.LayerNorm',
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sub_norm='nn.LayerNorm',
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epsilon=1e-6,
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out_channels=192,
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out_char_num=25,
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block_unit='Block',
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act='nn.GELU',
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last_stage=True,
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sub_num=2,
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prenorm=True,
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use_lenhead=False,
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**kwargs):
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super().__init__()
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self.img_size = img_size
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self.embed_dim = embed_dim
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self.out_channels = out_channels
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self.prenorm = prenorm
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patch_merging = None if patch_merging != 'Conv' and patch_merging != 'Pool' else patch_merging
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self.patch_embed = PatchEmbed(
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img_size=img_size,
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in_channels=in_channels,
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embed_dim=embed_dim[0],
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sub_num=sub_num)
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num_patches = self.patch_embed.num_patches
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self.HW = [img_size[0] // (2**sub_num), img_size[1] // (2**sub_num)]
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self.pos_embed = self.create_parameter(
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shape=[1, num_patches, embed_dim[0]], default_initializer=zeros_)
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self.add_parameter("pos_embed", self.pos_embed)
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self.pos_drop = nn.Dropout(p=drop_rate)
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Block_unit = eval(block_unit)
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dpr = np.linspace(0, drop_path_rate, sum(depth))
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self.blocks1 = nn.LayerList([
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Block_unit(
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dim=embed_dim[0],
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num_heads=num_heads[0],
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mixer=mixer[0:depth[0]][i],
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HW=self.HW,
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local_mixer=local_mixer[0],
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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act_layer=eval(act),
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attn_drop=attn_drop_rate,
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drop_path=dpr[0:depth[0]][i],
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norm_layer=norm_layer,
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epsilon=epsilon,
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prenorm=prenorm) for i in range(depth[0])
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])
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if patch_merging is not None:
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self.sub_sample1 = SubSample(
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embed_dim[0],
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embed_dim[1],
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sub_norm=sub_norm,
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stride=[2, 1],
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types=patch_merging)
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HW = [self.HW[0] // 2, self.HW[1]]
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else:
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HW = self.HW
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self.patch_merging = patch_merging
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self.blocks2 = nn.LayerList([
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Block_unit(
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dim=embed_dim[1],
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num_heads=num_heads[1],
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mixer=mixer[depth[0]:depth[0] + depth[1]][i],
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HW=HW,
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local_mixer=local_mixer[1],
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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act_layer=eval(act),
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attn_drop=attn_drop_rate,
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drop_path=dpr[depth[0]:depth[0] + depth[1]][i],
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norm_layer=norm_layer,
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epsilon=epsilon,
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prenorm=prenorm) for i in range(depth[1])
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])
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if patch_merging is not None:
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self.sub_sample2 = SubSample(
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embed_dim[1],
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embed_dim[2],
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sub_norm=sub_norm,
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stride=[2, 1],
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types=patch_merging)
|
|
HW = [self.HW[0] // 4, self.HW[1]]
|
|
else:
|
|
HW = self.HW
|
|
self.blocks3 = nn.LayerList([
|
|
Block_unit(
|
|
dim=embed_dim[2],
|
|
num_heads=num_heads[2],
|
|
mixer=mixer[depth[0] + depth[1]:][i],
|
|
HW=HW,
|
|
local_mixer=local_mixer[2],
|
|
mlp_ratio=mlp_ratio,
|
|
qkv_bias=qkv_bias,
|
|
qk_scale=qk_scale,
|
|
drop=drop_rate,
|
|
act_layer=eval(act),
|
|
attn_drop=attn_drop_rate,
|
|
drop_path=dpr[depth[0] + depth[1]:][i],
|
|
norm_layer=norm_layer,
|
|
epsilon=epsilon,
|
|
prenorm=prenorm) for i in range(depth[2])
|
|
])
|
|
self.last_stage = last_stage
|
|
if last_stage:
|
|
self.avg_pool = nn.AdaptiveAvgPool2D([1, out_char_num])
|
|
self.last_conv = nn.Conv2D(
|
|
in_channels=embed_dim[2],
|
|
out_channels=self.out_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
bias_attr=False)
|
|
self.hardswish = nn.Hardswish()
|
|
self.dropout = nn.Dropout(p=last_drop, mode="downscale_in_infer")
|
|
if not prenorm:
|
|
self.norm = eval(norm_layer)(embed_dim[-1], epsilon=epsilon)
|
|
self.use_lenhead = use_lenhead
|
|
if use_lenhead:
|
|
self.len_conv = nn.Linear(embed_dim[2], self.out_channels)
|
|
self.hardswish_len = nn.Hardswish()
|
|
self.dropout_len = nn.Dropout(
|
|
p=last_drop, mode="downscale_in_infer")
|
|
|
|
trunc_normal_(self.pos_embed)
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
zeros_(m.bias)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
zeros_(m.bias)
|
|
ones_(m.weight)
|
|
|
|
def forward_features(self, x):
|
|
x = self.patch_embed(x)
|
|
x = x + self.pos_embed
|
|
x = self.pos_drop(x)
|
|
for blk in self.blocks1:
|
|
x = blk(x)
|
|
if self.patch_merging is not None:
|
|
x = self.sub_sample1(
|
|
x.transpose([0, 2, 1]).reshape(
|
|
[0, self.embed_dim[0], self.HW[0], self.HW[1]]))
|
|
for blk in self.blocks2:
|
|
x = blk(x)
|
|
if self.patch_merging is not None:
|
|
x = self.sub_sample2(
|
|
x.transpose([0, 2, 1]).reshape(
|
|
[0, self.embed_dim[1], self.HW[0] // 2, self.HW[1]]))
|
|
for blk in self.blocks3:
|
|
x = blk(x)
|
|
if not self.prenorm:
|
|
x = self.norm(x)
|
|
return x
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
if self.use_lenhead:
|
|
len_x = self.len_conv(x.mean(1))
|
|
len_x = self.dropout_len(self.hardswish_len(len_x))
|
|
if self.last_stage:
|
|
if self.patch_merging is not None:
|
|
h = self.HW[0] // 4
|
|
else:
|
|
h = self.HW[0]
|
|
x = self.avg_pool(
|
|
x.transpose([0, 2, 1]).reshape(
|
|
[0, self.embed_dim[2], h, self.HW[1]]))
|
|
x = self.last_conv(x)
|
|
x = self.hardswish(x)
|
|
x = self.dropout(x)
|
|
if self.use_lenhead:
|
|
return x, len_x
|
|
return x
|